GHOSM: Graph-based Hybrid Outline and Skeleton Modelling for Shape Recognition

Abstract

An efficient and accurate shape detection model plays a major role in many research areas. With the emergence of more complex shapes in real-life applications, shape recognition models need to capture the structure with more effective features to achieve high accuracy rates for shape recognition. This article presents a new method for 2D/3D shape recognition based on graph spectral domain handcrafted features, which are formulated by exploiting both an outline and a skeleton shape through the global outline and internal details. A fully connected graph is generated over the shape outline to capture the global outline representation while a hierarchically clustered graph with adaptive connectivity is formed on the skeleton to capture the structural descriptions of the shape. We demonstrate the ability of the Fiedler vector to provide the graph partitioning of the skeleton graph. The performance evaluation demonstrates the efficiency of the proposed method compared to state-of-the-art studies with increments of 4.09%, 2.2%, and 14.02% for 2D static hand gestures, 2D shapes, and 3D shapes, respectively.